When you shop for a car, the first question is what model -- a Honda Civic for low-cost commuting, a Chevy Corvette for looking good and moving fast, or maybe a Ford F-150 to tote heavy loads. For the journey to AI, the most transformational technology of our time, the engine you need is a machine learning model. A machine learning model is an expression of an algorithm that combs through mountains of data to find patterns or make predictions. Fueled by data, ML models are the mathematical engines of AI. For example, an ML model for computer vision might be able to identify cars and pedestrians in a real-time video.

However, we have to terminate learning time in a reasonable value. In this example we define this ten thousand. Finally, we can make predictions I would like to predict for input instances directly and we need to define a method for that process predictions are made now we can dump these predictions for these inputs actual value is and we also need define a new variable we need to increase index our program is ready to run sorry we forgot yes! our machine learning classifier works with 100% accuracy expected 0, predicted 0 for 0 XOR 0 actual value is 1 and predicted value is also 1 for (0 XOR 1) and the other ones... So, we have developed a Exclusive OR classifier with tensorflow a hello world program I hope you guys enjoyed and understand Have a good day

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. OpenAI today released OpenAI Codex, its AI system that translates natural language into code, through an API in private beta. Able to understand more than a dozen programming languages, Codex can interpret commands in plain English and execute them, making it possible to build a natural language interface for existing apps. Codex powers Copilot, a GitHub service launched earlier this summer that provides suggestions for whole lines of code inside development environments like Microsoft Visual Studio. Codex is trained on billions of lines of public code and works with a broad set of frameworks and languages, adapting to the edits developers make to match their coding styles.

Code is the language that every computer uses to'speak' and'understand.' When developers write code, they make it to issue instructions to a computer and tell them what to do. But as technology has evolved and everything including the way we order food to eject satellites into space has moved to digital mode, the need for coding has drastically surged. But despite its increasing demand, we are still not able to take coding to laymen. When it comes to programming languages and coding, only tech-savvy can get their hands on it.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. The history of artificial intelligence is filled with theories and attempts to study and replicate the workings and structure of the brain. Symbolic AI systems tried to copy the brain's behavior through rule-based modules. Deep neural networks are designed after the neural activation patterns and wiring of the brain. But one idea that hasn't gotten enough attention from the AI community is how the brain creates itself, argues Peter Robin Hiesinger, Professor of Neurobiology at the Free University of Berlin (Freie Universität Berlin).

DEEP LEARNING, a form of artificial intelligence, powers voice assistants, facial recognition and music recommendations and underpins pioneering scientific research on how proteins fold. But all this was made possible by a breakthrough in a completely different field: video games. Powerful graphics chips (GPUs), developed to make video games more realistic, turned out to be ideal for speeding up the mathematical calculations used in deep learning. Host Tom Standage finds out how gaming transformed AI and meets the researchers who persevered when the chips were down. Subscribe to The Economist for full access to print, digital and audio editions: economist.com/podcastoffer

Deep Learning (DL) algorithms are emerging as a key alternative to computationally expensive CFD simulations. However, state-of-the-art DL approaches require large and high-resolution training data to learn accurate models. The size and availability of such datasets are a major limitation for the development of next-generation data-driven surrogate models for turbulent flows. This paper introduces SURFNet, a transfer learning-based super-resolution flow network. SURFNet primarily trains the DL model on low-resolution datasets and transfer learns the model on a handful of high-resolution flow problems - accelerating the traditional numerical solver independent of the input size. We propose two approaches to transfer learning for the task of super-resolution, namely one-shot and incremental learning. Both approaches entail transfer learning on only one geometry to account for fine-grid flow fields requiring 15x less training data on high-resolution inputs compared to the tiny resolution (64x256) of the coarse model, significantly reducing the time for both data collection and training. We empirically evaluate SURFNet's performance by solving the Navier-Stokes equations in the turbulent regime on input resolutions up to 256x larger than the coarse model. On four test geometries and eight flow configurations unseen during training, we observe a consistent 2-2.1x speedup over the OpenFOAM physics solver independent of the test geometry and the resolution size (up to 2048x2048), demonstrating both resolution-invariance and generalization capabilities. Our approach addresses the challenge of reconstructing high-resolution solutions from coarse grid models trained using low-resolution inputs (super-resolution) without loss of accuracy and requiring limited computational resources.

Realizing today's cloud-level artificial intelligence functionalities directly on devices distributed at the edge of the internet calls for edge hardware capable of processing multiple modalities of sensory data (e.g. video, audio) at unprecedented energy-efficiency. AI hardware architectures today cannot meet the demand due to a fundamental "memory wall": data movement between separate compute and memory units consumes large energy and incurs long latency. Resistive random-access memory (RRAM) based compute-in-memory (CIM) architectures promise to bring orders of magnitude energy-efficiency improvement by performing computation directly within memory. However, conventional approaches to CIM hardware design limit its functional flexibility necessary for processing diverse AI workloads, and must overcome hardware imperfections that degrade inference accuracy. Such trade-offs between efficiency, versatility and accuracy cannot be addressed by isolated improvements on any single level of the design. By co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present NeuRRAM - the first multimodal edge AI chip using RRAM CIM to simultaneously deliver a high degree of versatility for diverse model architectures, record energy-efficiency $5\times$ - $8\times$ better than prior art across various computational bit-precisions, and inference accuracy comparable to software models with 4-bit weights on all measured standard AI benchmarks including accuracy of 99.0% on MNIST and 85.7% on CIFAR-10 image classification, 84.7% accuracy on Google speech command recognition, and a 70% reduction in image reconstruction error on a Bayesian image recovery task. This work paves a way towards building highly efficient and reconfigurable edge AI hardware platforms for the more demanding and heterogeneous AI applications of the future.

The flexibility of decision boundaries in neural networks that are unguided by training data is a well-known problem typically resolved with generalization methods. A surprising result from recent knowledge distillation (KD) literature is that random, untrained, and equally structured teacher networks can also vastly improve generalization performance. It raises the possibility of existence of undiscovered assumptions useful for generalization on an uncertain region. In this paper, we shed light on the assumptions by analyzing decision boundaries and confidence distributions of both simple and KD-based generalization methods. Assuming that a decision boundary exists to represent the most general tendency of distinction on an input sample space (i.e., the simplest hypothesis), we show the various limitations of methods when using the hypothesis. To resolve these limitations, we propose matured dumb teacher based KD, conservatively transferring the hypothesis for generalization of the student without massive destruction of trained information. In practical experiments on feed-forward and convolution neural networks for image classification tasks on MNIST, CIFAR-10, and CIFAR-100 datasets, the proposed method shows stable improvement to the best test performance in the grid search of hyperparameters. The analysis and results imply that the proposed method can provide finer generalization than existing methods.

Most data is automatically collected and only ever "seen" by algorithms. Yet, data compressors preserve perceptual fidelity rather than just the information needed by algorithms performing downstream tasks. In this paper, we characterize the bit-rate required to ensure high performance on all predictive tasks that are invariant under a set of transformations, such as data augmentations. Based on our theory, we design unsupervised objectives for training neural compressors. Using these objectives, we train a generic image compressor that achieves substantial rate savings (more than $1000\times$ on ImageNet) compared to JPEG on 8 datasets, without decreasing downstream classification performance.